R language – HPCwirehttps://www.hpcwire.com
Since 1987 - Covering the Fastest Computers in the World and the People Who Run ThemThu, 17 Aug 2017 22:33:47 +0000en-UShourly1https://wordpress.org/?v=4.8.160365857SC10 Disruptive Technology Preview: The First Cloud Portal to “R” and Beyondhttps://www.hpcwire.com/2010/10/26/sc10_disruptive_technology_preview_the_first_cloud_portal_to_r_and_beyond/?utm_source=rss&utm_medium=rss&utm_campaign=sc10_disruptive_technology_preview_the_first_cloud_portal_to_r_and_beyond
https://www.hpcwire.com/2010/10/26/sc10_disruptive_technology_preview_the_first_cloud_portal_to_r_and_beyond/#respondTue, 26 Oct 2010 07:00:00 +0000http://www.hpcwire.com/?p=9251Each year at SC, a handful of disruptive technologies are selected as showcase items to represent drastic innovations for high-performance computing. On the list this year is a "Google Docs-like portal for scientific computing in the cloud" that delivers the first front-end door to the R language as well as a host of other useful features and tools for statisticians, scientists and the HPC community at large.

]]>At each annual Supercomputing Conference a handful of innovations are selected as the year’s “disruptive technologies” that are most likely to revolutionize high-performance computing. These are described as “drastic innovations in current practices…that have the potential to completely transform” the landscape.

At this year’s event in New Orleans, the focus will be on “new computing architectures and interfaces that will significantly impact the high-performance computing field throughout the next five to 15 years,” a focus that is reflected in the list of disruptive exhibitors who were selected by an SC committee.

Another “qualification” of those selected innovations is that they cannot have already emerged into the landscape in any meaningful way—that they sit on the bleeding edge waiting for impetus to burst forth and cause a paradigm shift.

At the edge of this potential sea-change in HPC—and included on that SC10 list of innovations this year is a one-man show run by Karim Chine of his newly-minted company, Cloud Era, Ltd.

Chine’s opportunity to showcase his “Google Docs-like portal for scientific computing in the cloud” could mean that his three-year effort, which he bootstrapped after he was unable to secure the funding needed for his research and development process, could garner some significant interest and make what this self-described “social entrepreneur” calls a real, universal impact in the broad field of large-scale data analysis.

Chine’s goal when he began the project after leaving academia was to bring the R language to the cloud and deliver it seamlessly to users who can share infrastructure and collaborate in real-time with a wide range of documents and computational tools. Or at least that’s the Reader’s Digest version–the actual technology and processes that create the experience for technical users goes far beyond these elements in terms of complexity and what is possible.

From the outset, Chine saw the inherent value of R as a ubiquitous tool but also recognized that there are a number of embedded challenges to using the language in terms of memory and compute capabilities being stretched to the limit. On the other end of the spectrum, he also saw how he could carry over lessons from social networks. Chine notes that part of what makes his Elastic-R project innovative–disruptive, even–is that users can move beyond sharing static information as they would on social networking platform and instead have a scientific network where real-time information sharing would be at the core of the communities.

The R Language Coming to a Browser Near You

It’s far too simple to suggest that what makes the platform unique or disruptive is the capacity for real-time resource and information-sharing. At the core of this innovation is the enhanced ability for researchers to use R, Scilab, and other tools in a new way–on the “infinite” resources provided by the cloud.

Many will agree that the R language is the lingua franca of data analysis—it’s the standard for nearly all statistics students in every major university and has a user base that some estimate is well over one million. In Chine’s view, the beauty of the R language, which is an open source implementation of S, lies “not just in statistics, not just in open source, it’s become the environment where people share scientific artifacts—where people contribute and access powerful tools for working with data.”

Although Chine discussed at length some of the benefits of the R language for scientists and researchers, he noted that there are some significant limitations to the language, particularly in the arena of software architecture and the R’s distinct lack of ability to optimize memory usage. However, the memory and architecture problems can be addressed by delivering R via cloud-based resources like EC2—in an environment where a user is no longer constrained by compute or memory and where inexpensive machine instances with 70 GB of RAM can be called into action in a few moments.

The idea of a “few moments” to get an instance up and running might strike some newer EC2 users as a little far-fetched, which leads to another issue that Elastic-R might be able to solve. One of the goals Chine had in mind was not only to provide a resource that would make R available via a web browser on a machine like an iPad, for instance, which has limited compute capacity, but to deliver the resource in a way that is intuitive and takes away from potential complexity in accessing remote infrastructure.

Elastic-R enables scientists, educators and students to use cloud resources seamlessly, work with R engines and use their full capabilities from within any standard web browser. For example, they can collaborate in real time, create, share and reuse machines, sessions, data functions, spreadsheets, dashboards, etc.”

Elastic-R is also an applications platform that allows anyone to assemble statistical methods and data with interactive user interfaces for the end user. These interfaces and dashboards are created visually and are automatically published and delivered as simple web applications.”

For Chine, the revolutionary or disruptive nature of Elastic-R lies in its user-friendliness, something that few people might say about the static R language. He states that offering a platform on top of R that is easy to work with in any browser allows people to access infrastructure without being computer savvy or with any real specific training. In essence, in three minutes you can have simple access to machines on EC2 that will allow you to do anything you want with large-scale data.

Even more disruptive, however, is the fact that users can hook in other scientific computing tools like Scilab or MATLAB thus making it a universal platform that is open to change and adds the possibility of throwing in additional tools to enhance research. They can then eliminate the problems involved with having their data in disparate formats that can complicate sharing by porting their results directly into standard Microsoft Office tools that can be shared and edited in real time via the web interface.

Taking R Beyond the Public Cloud

At the moment the resource can only be deployed using Amazon EC2 but this is simply a matter of how far Chine has traveled with his experiences—in theory, this can run on any resource. For instance, when he first began rolling out the prototype version of Elastic-R, he did so on the National Grid Services in the U.K. using a standard cluster, which would be possible on any other resource he might have selected.

The point is that what Chine has created is agnostic to the hardware and operating system, so users can connect to computational engines via their browsers, thus enabling to work with large-scale data that you don’t move, but can share with others for collaboration in real-time.

As Chine stated, “What’s wonderful about Amazon is that they already deliver the most significant public cloud of the moment, but also that they’ve blurred the frontier between normal computing and HPC…For the end user or interaction design perspective there’s no borderline between general computing and high-performance computing now.”

There are a range of capabilities that Elastic-R that are almost too numerous to mention in a relatively short article. In fact, this seems to be one of the reasons why this is such a disruptive technology; it’s multi-layered in its potential usefulness. Scientists and researchers can open mainstream computing environments beyond R (Scilab, SciPy, Sage, etc.) can issue commands to the remote R engne, install and deploy new packages, and easily run computationally-intensive algorithms virtually that are managed through the simple interface, then share all of it, including the computational resources themselves.

The following is from a slide out of the following deck (the presentation, which is the pptx file provides a more in-depth overview of the layers of the Elastic-R portal and what it provides) showing the onion-like way users can visualize their access to resources and tools.

During an interview with Karim Chine, I was granted access to the interface to watch how collaboration happens and how resources are secured. Without much experience at all, it was possible to understand intuitively understand exactly what was needed to get my job running, to indentify where the results were, who I could share them with and how at the exact same moment I updated a spreadsheet, my partner on the other side of the ocean could see my changes in real time. Real-time. There was no delay. The moment he replaced a “5” with a “6” on his end I saw it on my own browser screen.

This is big news for the future of scientific collaboration and computation using remote resources.

A Business Model Still in the Making

Chine’s goals are multi-layered and go far beyond making R more accessible to greater numbers of researchers via the cloud—he hopes to create a “Facebook” for scientists and statisticians where they can share and collaborate with big data in real time using a simple interface that they can build applications on top of and add or shed layers of computational tools and resources seamlessly.

As a social entrepreneur, Chine notes that this interface, as it develops, means that researchers in developing countries without access to high-performance computing resources can now easily create machine instances for small sums and even if those prices are too high, they can also share infrastructure with collaborating participants.

In essence, what this means is that there is not only an economy of information sharing involved with this disruptive innovation—there is an economic angle that allows researchers to extend their infrastructure to those across the world easily and in only a few moments.

As a business model, however, there are some issues that Chine admits he is still working to resolve. On the one hand, he sees the possibility of involving those who make scientific tools available, including The MathWorks, partnering in a revenue-sharing sense once those tools are integrated. He also sees value for supercomputing centers that might want to provide a simpler and more streamlined way to access and use high-performance computing infrastructure.

For now, however, he admits that he is just waiting to see how useful this will be as he extends his user base, which is currently only at 140 members—all of whom he knows personally. He will be announcing the technology just before SC10 as publicly available.

While the cloud can open the doors to enhanced collaboration and resource sharing as well as providing the tools researchers need, there is a remaining need for software that creates a sturdy bridge between the tools for scientific computation and the cloud, which is where Elastic-R fits into the picture.

Coupled with the open, collaborative nature of the project, which is driven by its social entrepreneur founder and creator, it will be thrilling indeed to watch how the community receives, uses, then builds on this disruptive innovation.

]]>https://www.hpcwire.com/2010/10/26/sc10_disruptive_technology_preview_the_first_cloud_portal_to_r_and_beyond/feed/09251Revolution Analytics Lifts R Language into Terascale Computinghttps://www.hpcwire.com/2010/08/04/revolution_analytics_lifts_r_language_into_terascale_computing/?utm_source=rss&utm_medium=rss&utm_campaign=revolution_analytics_lifts_r_language_into_terascale_computing
https://www.hpcwire.com/2010/08/04/revolution_analytics_lifts_r_language_into_terascale_computing/#respondWed, 04 Aug 2010 07:00:00 +0000http://www.hpcwire.com/?p=5160R language booster Revolution Analytics is going after the predictive analytics crowd with its latest Revolution R Enterprise software platform. The company announced this week it will be introducing a package called RevoScaleR to bring the R language into the world of "Big Data," enabling analytics applications to turbo-charge their performance and scale terabyte-sized mountains of data.

]]>R language booster Revolution Analytics is going after the predictive analytics crowd with its latest Revolution R Enterprise software platform. The company announced this week it will be introducing a package called RevoScaleR to bring the R language into the world of “Big Data,” enabling analytics applications to turbo-charge their performance and scale terabyte-sized mountains of data.

Analytics has increasingly become a way for companies to automate intelligence. Businesses in quantitative finance, life sciences, telecom, manufacturing and retail are all looking to mine their data for profits. Governments are also generating enormous amounts of data, and are looking for ways to make sense of it all. Organizations traditionally looked to SAS and SPSS (now a part of IBM) to provide high-end analytics, but a new ecosystem is growing up around the open-source R language, a framework used for statistical computing and modeling.

Developed in the 1990s by Ross Ihaka and Robert Gentleman in New Zealand, the R language was purpose-built for the needs of statisticians. As such, it is tailor-made for analytics and has become the most popular programming language for such work in academia and, increasingly, in the commercial realm. “It’s really has become the lingua franca of learning statistics at universities,” says Jeff Erhardt, COO at Revolution Analytics.

Because of its open-source nature, R is attracting a lot of innovation from its user community. Erhardt says there are probably close to 2 million users worldwide today, and that number is growing. His company hopes to turn that grassroots popularity into a thriving business by propelling the language into the enterprise.

To accomplish that will require some work. R has two fundamental limitations. First, the language is memory bound. That is, it expects the entire database to be in RAM. For the typical workstation, that becomes a problem for any dataset over a few gigabytes. Second is performance. R executes a single process, so cannot take advantage of the performance inherent in multicore/multithreaded CPUs and cluster architectures. According to Revolution Analytics CTO David Champagne, to make it into the enterprise, both issues have to be addressed. And that’s what Revolution R Enterprise and the new RevoScaleR package aim to do.

Speed is really the big issue here, given that the results of predictive analytics are time-sensitive to one degree or another. For example, a trading desk in the US needs to be ready to execute the optimal trades and arbitrage opportunities when the markets in Tokyo open in the morning. To do that, the trading institution has to be able to churn through its entire portfolio overnight.

Overcoming the memory limitation has been accomplished with what the company calls its “external memory” framework. Essentially, it allows data to be quickly brought into memory in bite-sized chunks so that even terabyte-sized data files with billions of rows can be accommodated. To support this model, Revolution Analytics invented the XDF file format in which data rows and columns can be read and written in arbitrary blocks. In fact, new columns and rows can even be inserted on the fly without having to rewrite the rest of the file. This speeds up data transformations considerably, according to Champagne, and makes the analytics workflow much more efficient.

A lot of the execution speed is the result of good old-fashioned parallelism. The initial RevoScaleR implementation enables R applications to be parallelized across multiple cores (and CPUs) on a laptop, workstation or server. With a dual-socket Intel Xeon 5600 (Westmere) server, that means computation can be distributed across as many as 12 cores. Support for distributing an app across multiple nodes in a datacenter will follow shortly. RevoScaleR provides an interface for a number of common statistical algorithms including linear regression, cross tabulation, logistic regression, and summary statistics, with more on the way.

The company has demonstrated considerable speedups using the RevoScaleR package. On an 8-core Nehalem server, with 8 GB of RAM, they were able to process a 13 GB file in record time. In this case the file contained US airline flight data from 1987 to 2008 and was made up of 123 million rows and 29 columns. They were able to execute a linear regression on two variables (arrival delay and day of the week) in about 1 second. The next best implementation (using a special R package to deal with big data files) took around six minutes.

Specific comparisons against traditional SAS and SPSS implementations are lacking, but according to Champagne, beta customers using RevoScaleR have reported orders of magnitude performance speedup compared to legacy analytics platforms. And although Erhardt claims they are not specifically going after SAS and SPSS accounts, customers looking for a less proprietary solution might be tempted by the Revolution offering. “Clearly they come to us, in particular, when they are looking for cost advantage,” he says.

The company basically has two tiers of pricing for commercial customers (Revolution R Enterprise is free to academic users). For the individual user on a desktop, they’re going to charge in “the low thousands of dollars.” The second tier is for multiple users in a more typical enterprise server-based setup. Depending on the configuration, prices should be in the low-five figure range, with a site license in the six-figure range. According to Erhardt, the goal is to leverage the open-source R software and offer their enterprise product at a fraction of the price of traditional analytics software platforms.

The initial RevoScaleR package will be available in 30 days, but only with multicore/multiprocessor support, and only on Windows. Support for distributed computing across a cluster and on Linux is slated for sometime in the next quarter. Also in the queue is support for C++ users who want to add their home-grown algorithms that take advantage of RevoScaleR’s external memory model. And last on the docket is a Web services product that will make R applications accessible from a browser or some other client interface. For a more detailed look at what’s in store, check out the company’s white paper of its roadmap.

]]>I often like to write about topical issues, particularly those may have far reaching effects on the HPC market. When in the “HPC dry season,” I often turn to the next best thing; programming parallel computers. When I discuss parallel programming, I often talk about functional approaches versus imperative approaches.

]]>Today science is big business. It’s now subject to all kinds of commercial and political pressure and external formal controls. The deluge of data is, like the social web, threatening to suffocate us. The combination of data explosion and external control makes it utterly imperative that information is utterly free—in terms of access. Access to see the raw data, the computer models and the control and flow of information.

]]>https://www.hpcwire.com/2010/03/09/can_free_software_drive_the_fourth_paradigm/feed/05390Expert Analysis: You Can Predict That R Will Succeedhttps://www.hpcwire.com/2010/03/03/expert_analysis_you_can_predict_that_r_will_succeed/?utm_source=rss&utm_medium=rss&utm_campaign=expert_analysis_you_can_predict_that_r_will_succeed
https://www.hpcwire.com/2010/03/03/expert_analysis_you_can_predict_that_r_will_succeed/#respondWed, 03 Mar 2010 08:00:00 +0000http://www.hpcwire.com/?p=5381REvolution Computing may do for R what RedHat did for Linux.

]]>As more organizations recognize the importance of using data as a strategic asset, the tantalizing power to predict customer behavior, uncover fraud, anticipate manufacturing hiccups and speed insight from big data will drive mainstream interest in going beyond standard business intelligence and data warehousing.